The chapter on computational toxicology tools provides a basic exploration of their roles in evaluating chemicals in food. Some of these chemicals may pose health risks necessitating rigorous safety assessments. Computational tools may support these assessments by swiftly screening chemicals based on their structural properties, predicting their toxicity, and integrating various data sources. Key platforms such as the OECD QSAR Toolbox, along with other publicly and commercially available tools, facilitate safety and hazard assessments and aid in regulatory compliance. Yet, despite their benefits in reducing animal testing, accelerating assessments, and enhancing transparency, these tools encounter challenges related to data quality and predictive accuracy. The author advocates for a synergistic approach that combines computational predictions with experimental data to bolster safety and hazard assessments, highlights emerging tools, and provides information to aid in selecting appropriate ones based on specific endpoints and regulatory requirements, thereby ensuring that advancements in food safety align with technological innovations and regulatory standards.